Identifying timescales and possible precursors of the awake to asleep transition in EOG time series

In this work we study the awake to asleep state transition in eye blinking activity. In this perspective the human electroculographic activity (EOG) was first experimentally investigated by means of a spectral analyses of the time series resulting for processes underlying both the brain activity and the eye dynamics. We studied the evolution of the spectral content both via the classical spectrogram and with the computation of summarizing scalar parameters: mean frequency, maximum frequency, spectral variance. With these tools we highlighted a significative dynamical change appearing before the transition from the awake to the asleep state, characterized by a general widening of the spectrum, that translates into a decrease of the maximum frequency, an increase of the average frequency and an increase of the spectral variance. Due to inherently high non-linearities involved, chaotic patterns were likely to occur in the experimental time series. These were analyzed therefore with the chaos theory. In particular we studied the time evolution of dynamical parameters as computed on different windows of the time series, i.e. optimal delay time as suggested by autocorrelation and mutual information on one side, embedding quality evaluation as suggested by the False Nearest Neighbours percentage on the other.